The article titled "Functional Scaling Laws in Kernel Regression: Loss Dynamics and Learning Rate Schedules" examines the behavior of loss dynamics within kernel regression models, emphasizing the role of learning rate schedules. It identifies a notable gap in existing research, which predominantly concentrates on the final-step loss rather than the entire training trajectory. To address this, the study employs a theoretical framework grounded in stochastic gradient descent to analyze how loss evolves over time during training. This approach provides a more comprehensive understanding of the learning process beyond static end-point measures. The focus on kernel regression situates the work within the broader context of machine learning research, particularly in understanding scaling laws that govern model performance. The article contributes to ongoing discussions by offering insights into how learning rate adjustments impact loss dynamics, potentially informing more effective training strategies. This research aligns with recent studies exploring similar themes in AI and machine learning, as indicated by connected coverage within the field.